library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
library(TH.data)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
All variables are derived from a laser scanning image of the eye background taken by the Heidelberg Retina Tomograph. Most of the variables describe either the area or volume in certain parts of the papilla and are measured in four sectors (temporal, superior, nasal and inferior) as well as for the whole papilla (global). The global measurement is, roughly, the sum of the measurements taken in the four sector.
The observations in both groups are matched by age and sex to prevent any bias.
Source Torsten Hothorn and Berthold Lausen (2003), Double-Bagging: Combining classifiers by bootstrap aggregation. Pattern Recognition, 36(6), 1303–1309.
GlaucomaM {TH.data}
data("GlaucomaM")
pander::pander(table(GlaucomaM$Class))
| glaucoma | normal |
|---|---|
| 98 | 98 |
GlaucomaM$Class <- 1*(GlaucomaM$Class=="glaucoma")
studyName <- "GlaucomaM"
dataframe <- GlaucomaM
outcome <- "Class"
thro <- 0.80
TopVariables <- 10
cexheat = 0.25
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 196 | 62 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 98 | 98 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) > 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.9961105
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> Included: 62 , Uni p: 0.03074051 , Uncorrelated Base: 6 , Outcome-Driven Size: 0 , Base Size: 6
#>
#>
1 <R=0.996,r=0.973,N= 8>, Top: 2( 5 )[ 1 : 2 Fa= 2 : 0.973 ]( 2 , 6 , 0 ),<|>Tot Used: 8 , Added: 6 , Zero Std: 0 , Max Cor: 0.971
#>
2 <R=0.971,r=0.961,N= 8>, Top: 6( 1 )[ 1 : 6 Fa= 8 : 0.961 ]( 6 , 6 , 2 ),<|>Tot Used: 20 , Added: 6 , Zero Std: 0 , Max Cor: 0.958
#>
3 <R=0.958,r=0.954,N= 8>, Top: 1( 2 )[ 1 : 1 Fa= 9 : 0.954 ]( 1 , 2 , 8 ),<|>Tot Used: 23 , Added: 2 , Zero Std: 0 , Max Cor: 0.952
#>
4 <R=0.952,r=0.926,N= 17>, Top: 6( 3 )[ 1 : 6 Fa= 10 : 0.926 ]( 6 , 11 , 9 ),<|>Tot Used: 35 , Added: 11 , Zero Std: 0 , Max Cor: 0.920
#>
5 <R=0.920,r=0.910,N= 17>, Top: 4( 1 )[ 1 : 4 Fa= 10 : 0.910 ]( 4 , 6 , 10 ),<|>Tot Used: 38 , Added: 6 , Zero Std: 0 , Max Cor: 0.910
#>
6 <R=0.910,r=0.905,N= 17>, Top: 2( 1 )[ 1 : 2 Fa= 12 : 0.905 ]( 2 , 2 , 10 ),<|>Tot Used: 38 , Added: 2 , Zero Std: 0 , Max Cor: 0.980
#>
7 <R=0.980,r=0.890,N= 4>, Top: 2( 1 )[ 1 : 2 Fa= 13 : 0.890 ]( 2 , 2 , 12 ),<|>Tot Used: 38 , Added: 2 , Zero Std: 0 , Max Cor: 0.941
#>
8 <R=0.941,r=0.821,N= 30>, Top: 12( 1 )[ 1 : 12 Fa= 20 : 0.821 ]( 12 , 16 , 13 ),<|>Tot Used: 52 , Added: 16 , Zero Std: 0 , Max Cor: 0.819
#>
9 <R=0.819,r=0.800,N= 30>, Top: 1( 2 )[ 1 : 1 Fa= 20 : 0.800 ]( 1 , 2 , 20 ),<|>Tot Used: 52 , Added: 2 , Zero Std: 0 , Max Cor: 0.799
#>
10 <R=0.799,r=0.800,N= 0>
#>
[ 10 ], 0.798676 Decor Dimension: 52 Nused: 52 . Cor to Base: 23 , ABase: 3 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
3.03
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
1.04
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
3.94
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
2.88
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPSTM <- attr(DEdataframe,"UPSTM")
gplots::heatmap.2(1.0*(abs(UPSTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after IDeA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.799364
if (nrow(dataframe) < 1000)
{
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}
if (nrow(dataframe) < 1000)
{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##topfive
topvar <- c(1:length(varlist)) <= TopVariables
pander::pander(univarRAW$orderframe[topvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| vari | 0.04480 | 0.0368 | 0.1150 | 0.0543 | 0.1035 | 0.880 |
| varg | 0.17851 | 0.1170 | 0.4139 | 0.1967 | 0.0252 | 0.873 |
| vars | 0.04506 | 0.0314 | 0.1068 | 0.0596 | 0.0172 | 0.851 |
| tmi | 0.03664 | 0.1258 | -0.1097 | 0.1038 | 0.6980 | 0.832 |
| varn | 0.08224 | 0.0595 | 0.1774 | 0.0894 | 0.2533 | 0.830 |
| hic | 0.39863 | 0.1407 | 0.2114 | 0.1574 | 0.7659 | 0.822 |
| tmg | -0.03356 | 0.0885 | -0.1524 | 0.0927 | 0.5179 | 0.818 |
| phcg | -0.03546 | 0.0691 | -0.1216 | 0.0661 | 0.9359 | 0.818 |
| phci | 0.00898 | 0.0882 | -0.0937 | 0.0779 | 0.7638 | 0.814 |
| tms | 0.03959 | 0.1166 | -0.1192 | 0.1376 | 0.9756 | 0.808 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
theLaVar <- rownames(finalTable)[str_detect(rownames(finalTable),"La_")]
pander::pander(univarDe$orderframe[topLAvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| varg | 0.17851 | 0.11700 | 0.41387 | 0.19669 | 0.025181 | 0.873 |
| La_eag | -0.35521 | 0.24722 | -0.74028 | 0.39257 | 0.498588 | 0.826 |
| tmg | -0.03356 | 0.08846 | -0.15241 | 0.09266 | 0.517931 | 0.818 |
| phcg | -0.03546 | 0.06909 | -0.12159 | 0.06612 | 0.935907 | 0.818 |
| La_vbrt | -0.01055 | 0.00781 | -0.01926 | 0.00971 | 0.217120 | 0.806 |
| rnf | 0.13956 | 0.06764 | 0.22520 | 0.09753 | 0.225229 | 0.800 |
| phcn | 0.00692 | 0.07364 | -0.07168 | 0.08814 | 0.303322 | 0.796 |
| vart | 0.00640 | 0.00533 | 0.01460 | 0.01166 | 0.000754 | 0.777 |
| La_abrg | -0.45638 | 0.22776 | -0.70578 | 0.33111 | 0.260305 | 0.774 |
| mhcg | 0.12197 | 0.05958 | 0.06633 | 0.06596 | 0.925585 | 0.756 |
| La_vbri | 0.00920 | 0.01487 | -0.00246 | 0.01346 | 0.216854 | 0.745 |
| La_hic | -0.04138 | 0.07090 | -0.09946 | 0.08204 | 0.545713 | 0.721 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
theSigDc <- dc[theLaVar]
names(theSigDc) <- NULL
theSigDc <- unique(names(unlist(theSigDc)))
theFormulas <- dc[rownames(finalTable)]
deFromula <- character(length(theFormulas))
names(deFromula) <- rownames(finalTable)
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 2.24 | 45 | 0.726 |
allSigvars <- names(dc)
dx <- names(deFromula)[1]
for (dx in names(deFromula))
{
coef <- theFormulas[[dx]]
cname <- names(theFormulas[[dx]])
names(cname) <- cname
for (cf in names(coef))
{
if (cf != dx)
{
if (coef[cf]>0)
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("+ %5.3f*%s",coef[cf],cname[cf]))
}
else
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("%5.3f*%s",coef[cf],cname[cf]))
}
}
}
}
finalTable <- rbind(finalTable,univarRAW$orderframe[theSigDc[!(theSigDc %in% rownames(finalTable))],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- deFromula[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| varg | 0.17851 | 0.11700 | 0.41387 | 0.19669 | 2.52e-02 | 0.873 | 0.873 | 3 | |
| La_eag | -0.929ag + 1.000eag | -0.35521 | 0.24722 | -0.74028 | 0.39257 | 4.99e-01 | 0.826 | 0.664 | 4 |
| hic | NA | 0.39863 | 0.14073 | 0.21139 | 0.15743 | 7.66e-01 | 0.822 | 0.822 | NA |
| tmg | -0.03356 | 0.08846 | -0.15241 | 0.09266 | 5.18e-01 | 0.818 | 0.818 | 2 | |
| phcg | -0.03546 | 0.06909 | -0.12159 | 0.06612 | 9.36e-01 | 0.818 | 0.818 | NA | |
| La_vbrt | + 0.084vbsg -0.947vbst -0.090vbrg + 1.000vbrt | -0.01055 | 0.00781 | -0.01926 | 0.00971 | 2.17e-01 | 0.806 | 0.709 | -2 |
| vbri | NA | 0.14660 | 0.08893 | 0.06443 | 0.08073 | 2.88e-04 | 0.803 | 0.803 | NA |
| rnf | 0.13956 | 0.06764 | 0.22520 | 0.09753 | 2.25e-01 | 0.800 | 0.800 | NA | |
| phcn | 0.00692 | 0.07364 | -0.07168 | 0.08814 | 3.03e-01 | 0.796 | 0.796 | NA | |
| vart | 0.00640 | 0.00533 | 0.01460 | 0.01166 | 7.54e-04 | 0.777 | 0.777 | NA | |
| La_abrg | -0.999eag + 1.000abrg | -0.45638 | 0.22776 | -0.70578 | 0.33111 | 2.60e-01 | 0.774 | 0.758 | 2 |
| vbrg | NA | 0.55871 | 0.35593 | 0.29249 | 0.43476 | 9.07e-06 | 0.771 | 0.771 | NA |
| abrg | NA | 1.60782 | 0.64702 | 0.97601 | 0.78552 | 2.05e-01 | 0.758 | 0.758 | NA |
| mhcg | 0.12197 | 0.05958 | 0.06633 | 0.06596 | 9.26e-01 | 0.756 | 0.756 | 3 | |
| emd | NA | 0.36204 | 0.12231 | 0.25577 | 0.11134 | 7.61e-01 | 0.746 | 0.746 | 5 |
| La_vbri | + 0.189vbsg -0.799vbsi -0.213vbrg + 1.000vbri | 0.00920 | 0.01487 | -0.00246 | 0.01346 | 2.17e-01 | 0.745 | 0.803 | -2 |
| vbsi | NA | 0.20517 | 0.09529 | 0.12323 | 0.09254 | 6.57e-02 | 0.742 | 0.742 | NA |
| vbsg | NA | 0.77012 | 0.37208 | 0.49666 | 0.40027 | 1.38e-01 | 0.724 | 0.724 | NA |
| La_hic | + 1.000hic -1.215emd | -0.04138 | 0.07090 | -0.09946 | 0.08204 | 5.46e-01 | 0.721 | 0.822 | -1 |
| vbrt | NA | 0.12276 | 0.08051 | 0.07163 | 0.06635 | 4.26e-02 | 0.709 | 0.709 | NA |
| eag | NA | 2.06546 | 0.57098 | 1.68282 | 0.80355 | 4.00e-01 | 0.664 | 0.664 | NA |
| vbst | NA | 0.15583 | 0.08263 | 0.11215 | 0.07113 | 4.71e-01 | 0.662 | 0.662 | NA |
| ag | NA | 2.60522 | 0.53920 | 2.60784 | 0.76445 | 2.79e-01 | 0.477 | 0.477 | 6 |
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,tol=0.002) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 83 | 15 |
| 1 | 11 | 87 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.520 | 0.4481 | 0.592 |
| tp | 0.500 | 0.4279 | 0.572 |
| se | 0.888 | 0.8080 | 0.943 |
| sp | 0.847 | 0.7601 | 0.912 |
| diag.ac | 0.867 | 0.8117 | 0.911 |
| diag.or | 43.764 | 19.0046 | 100.778 |
| nndx | 1.361 | 1.1705 | 1.760 |
| youden | 0.735 | 0.5682 | 0.854 |
| pv.pos | 0.853 | 0.7691 | 0.915 |
| pv.neg | 0.883 | 0.8003 | 0.940 |
| lr.pos | 5.800 | 3.6213 | 9.289 |
| lr.neg | 0.133 | 0.0755 | 0.233 |
| p.rout | 0.480 | 0.4079 | 0.552 |
| p.rin | 0.520 | 0.4481 | 0.592 |
| p.tpdn | 0.153 | 0.0883 | 0.240 |
| p.tndp | 0.112 | 0.0574 | 0.192 |
| p.dntp | 0.147 | 0.0847 | 0.231 |
| p.dptn | 0.117 | 0.0599 | 0.200 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 87 | 15 | 102 |
| Test - | 11 | 83 | 94 |
| Total | 98 | 98 | 196 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.867 | 0.812 | 0.911 |
| 3 | se | 0.888 | 0.808 | 0.943 |
| 4 | sp | 0.847 | 0.760 | 0.912 |
| 6 | diag.or | 43.764 | 19.005 | 100.778 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 85 | 13 |
| 1 | 11 | 87 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.510 | 0.4380 | 0.582 |
| tp | 0.500 | 0.4279 | 0.572 |
| se | 0.888 | 0.8080 | 0.943 |
| sp | 0.867 | 0.7838 | 0.927 |
| diag.ac | 0.878 | 0.8233 | 0.920 |
| diag.or | 51.713 | 21.9537 | 121.814 |
| nndx | 1.324 | 1.1494 | 1.690 |
| youden | 0.755 | 0.5919 | 0.870 |
| pv.pos | 0.870 | 0.7880 | 0.929 |
| pv.neg | 0.885 | 0.8042 | 0.941 |
| lr.pos | 6.692 | 4.0142 | 11.157 |
| lr.neg | 0.129 | 0.0738 | 0.227 |
| p.rout | 0.490 | 0.4179 | 0.562 |
| p.rin | 0.510 | 0.4380 | 0.582 |
| p.tpdn | 0.133 | 0.0726 | 0.216 |
| p.tndp | 0.112 | 0.0574 | 0.192 |
| p.dntp | 0.130 | 0.0711 | 0.212 |
| p.dptn | 0.115 | 0.0586 | 0.196 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 87 | 13 | 100 |
| Test - | 11 | 85 | 96 |
| Total | 98 | 98 | 196 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.878 | 0.823 | 0.920 |
| 3 | se | 0.888 | 0.808 | 0.943 |
| 4 | sp | 0.867 | 0.784 | 0.927 |
| 6 | diag.or | 51.713 | 21.954 | 121.814 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 77 | 21 |
| 1 | 8 | 90 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.5663 | 0.4938 | 0.637 |
| tp | 0.5000 | 0.4279 | 0.572 |
| se | 0.9184 | 0.8455 | 0.964 |
| sp | 0.7857 | 0.6913 | 0.862 |
| diag.ac | 0.8520 | 0.7945 | 0.899 |
| diag.or | 41.2500 | 17.2939 | 98.391 |
| nndx | 1.4203 | 1.2102 | 1.863 |
| youden | 0.7041 | 0.5368 | 0.826 |
| pv.pos | 0.8108 | 0.7255 | 0.879 |
| pv.neg | 0.9059 | 0.8229 | 0.958 |
| lr.pos | 4.2857 | 2.9201 | 6.290 |
| lr.neg | 0.1039 | 0.0531 | 0.203 |
| p.rout | 0.4337 | 0.3632 | 0.506 |
| p.rin | 0.5663 | 0.4938 | 0.637 |
| p.tpdn | 0.2143 | 0.1378 | 0.309 |
| p.tndp | 0.0816 | 0.0359 | 0.155 |
| p.dntp | 0.1892 | 0.1211 | 0.275 |
| p.dptn | 0.0941 | 0.0415 | 0.177 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 90 | 21 | 111 |
| Test - | 8 | 77 | 85 |
| Total | 98 | 98 | 196 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.852 | 0.794 | 0.899 |
| 3 | se | 0.918 | 0.845 | 0.964 |
| 4 | sp | 0.786 | 0.691 | 0.862 |
| 6 | diag.or | 41.250 | 17.294 | 98.391 |
par(op)
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 79 | 19 |
| 1 | 10 | 88 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.546 | 0.4734 | 0.617 |
| tp | 0.500 | 0.4279 | 0.572 |
| se | 0.898 | 0.8203 | 0.950 |
| sp | 0.806 | 0.7139 | 0.879 |
| diag.ac | 0.852 | 0.7945 | 0.899 |
| diag.or | 36.589 | 16.0544 | 83.391 |
| nndx | 1.420 | 1.2062 | 1.872 |
| youden | 0.704 | 0.5343 | 0.829 |
| pv.pos | 0.822 | 0.7367 | 0.890 |
| pv.neg | 0.888 | 0.8031 | 0.945 |
| lr.pos | 4.632 | 3.0762 | 6.973 |
| lr.neg | 0.127 | 0.0698 | 0.230 |
| p.rout | 0.454 | 0.3830 | 0.527 |
| p.rin | 0.546 | 0.4734 | 0.617 |
| p.tpdn | 0.194 | 0.1210 | 0.286 |
| p.tndp | 0.102 | 0.0500 | 0.180 |
| p.dntp | 0.178 | 0.1104 | 0.263 |
| p.dptn | 0.112 | 0.0552 | 0.197 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 88 | 19 | 107 |
| Test - | 10 | 79 | 89 |
| Total | 98 | 98 | 196 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.852 | 0.794 | 0.899 |
| 3 | se | 0.898 | 0.820 | 0.950 |
| 4 | sp | 0.806 | 0.714 | 0.879 |
| 6 | diag.or | 36.589 | 16.054 | 83.391 |
par(op)